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dc.contributor.authorGholami, Raoof
dc.contributor.authorFakhari, N.
dc.date.accessioned2017-11-24T05:25:30Z
dc.date.available2017-11-24T05:25:30Z
dc.date.created2017-11-24T04:48:46Z
dc.date.issued2017
dc.identifier.citationGholami, R. and Fakhari, N. 2017. Support Vector Machine: Principles, Parameters, and Applications. In Handbook of Neural Computation, 515-535.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/58416
dc.identifier.doi10.1016/B978-0-12-811318-9.00027-2
dc.description.abstract

© 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the late 1990s and successfully applied to many engineering related applications. In this chapter, attempts were made to introduce the SVM, its principles, structures, and parameters. The issue of selecting a kernel function and other associated parameters of SVMs was also raised and applications from different petroleum and mining related tasks were brought to show how those parameters can be properly selected. It seems that the cross-validation approach would be the best technique for parameter selections of SVMs but few other concerns such as running time must not be neglected.

dc.titleSupport Vector Machine: Principles, Parameters, and Applications
dc.typeBook Chapter
dcterms.source.startPage515
dcterms.source.endPage535
dcterms.source.titleHandbook of Neural Computation
dcterms.source.isbn9780128113196
curtin.departmentCurtin Malaysia
curtin.accessStatusFulltext not available


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